Present day organizations generate and hold a lot of data about their customers. This data is converted into information which fuels the organization's decision making, campaign management solution and also guide the business processes. There are loads of data which organizations hold but do not completely exploit for example customer behavior and their preferences to the products that organization does not sell. Further the organization does not sell the available quality information with them. Although the same data which is of no direct significance to the organization can provide vital insights to the local businesses having little information about customers in the area they operate in. Some examples of such information are the call records and web logs, banking and credit card transactions, EMI and utility bills, etc.
There is limited capability available currently for organization to let external B2B customer utilize the information they store. In prior art instances organizations have published summarized reports on their customers buying patterns or behaviors which can be referenced by the external B2B customer for building or marketing their products. However this method has a limitation that external B2B customers can only make an inference in customer profile but cannot specifically target those customers which further results in the waste of advertising spend and reduces the return on investment from the advertising.
Presently available prior art techniques publish the high level data with external B2B users. However, this information just provides a guideline to the users. It does not enable them to specifically target those customers. Also these techniques fail to take care of privacy protection of the customer data. No easy-to-use and intuitive way is available to external B2B customers where they can analyze the information and can target those specific customers.
Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.